{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import texthero as hero\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['text_clean'] = hero.clean(df['text'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "pd = pd.read_csv(\n",
    "   \"https://github.com/jbesomi/texthero/raw/master/dataset/bbcsport.csv\"\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>text</th>\n",
       "      <th>topic</th>\n",
       "      <th>pca</th>\n",
       "      <th>text_clean</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
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       "      <td>claxton hunting first major medal british hurd...</td>\n",
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       "      <th>1</th>\n",
       "      <td>O'Sullivan could run in Worlds\\n\\nSonia O'Sull...</td>\n",
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       "                                                text      topic  \\\n",
       "0  Claxton hunting first major medal\\n\\nBritish h...  athletics   \n",
       "1  O'Sullivan could run in Worlds\\n\\nSonia O'Sull...  athletics   \n",
       "\n",
       "                                            pca  \\\n",
       "0    [0.02183185305815857, -0.2552054343867421]   \n",
       "1  [-0.10784133272210035, 0.012972525804132487]   \n",
       "\n",
       "                                          text_clean  \n",
       "0  claxton hunting first major medal british hurd...  \n",
       "1  sullivan could run worlds sonia sullivan indic...  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['text_clean'] = hero.clean(df['text'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['text_clean'] = df['text'].pipe(hero.clean)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "from texthero import preprocessing\n",
    "\n",
    "custom_pipeline = [preprocessing.fillna,\n",
    "                   preprocessing.lowercase,\n",
    "                   preprocessing.remove_whitespace]\n",
    "df['clean_text'] = hero.clean(df['text'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['clean_text'] = df['clean_text'].pipe(hero.clean, custom_pipeline)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['tfidf_clean_text'] = hero.tfidf(df['clean_text'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['pca_tfidf_clean_text'] = hero.pca(df['tfidf_clean_text'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['pca'] = (\n",
    "            df['text']\n",
    "            .pipe(hero.clean)\n",
    "            .pipe(hero.tfidf)\n",
    "            .pipe(hero.pca)\n",
    "   )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
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   "source": [
    "hero.scatterplot(df, col='pca', color='topic', title=\"PCA BBC Sport news\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
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    {
     "data": {
      "text/plain": [
       "topic         \n",
       "athletics  the    1731\n",
       "           to      889\n",
       "           in      888\n",
       "           and     667\n",
       "           a       615\n",
       "cricket    the    2248\n",
       "           to     1310\n",
       "           a      1046\n",
       "           in     1012\n",
       "           and     990\n",
       "football   the    4519\n",
       "           to     2644\n",
       "           a      2064\n",
       "           and    1912\n",
       "           in     1585\n",
       "rugby      the    2772\n",
       "           to     1401\n",
       "           a      1211\n",
       "           and    1129\n",
       "           in     1102\n",
       "tennis     the    1528\n",
       "           to      826\n",
       "           in      707\n",
       "           a       588\n",
       "           I       584\n",
       "Name: text, dtype: int64"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
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   "source": [
    "NUM_TOP_WORDS = 5\n",
    "df.groupby('topic')['text'].apply(lambda x: hero.top_words(x)[:NUM_TOP_WORDS])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
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\"#E5ECF6\", \"caxis\": {\"gridcolor\": \"white\", \"linecolor\": \"white\", \"ticks\": \"\"}}, \"title\": {\"x\": 0.05}, \"xaxis\": {\"automargin\": true, \"gridcolor\": \"white\", \"linecolor\": \"white\", \"ticks\": \"\", \"title\": {\"standoff\": 15}, \"zerolinecolor\": \"white\", \"zerolinewidth\": 2}, \"yaxis\": {\"automargin\": true, \"gridcolor\": \"white\", \"linecolor\": \"white\", \"ticks\": \"\", \"title\": {\"standoff\": 15}, \"zerolinecolor\": \"white\", \"zerolinewidth\": 2}}}, \"title\": {\"text\": \"PCA BBC Sport news\"}, \"xaxis\": {\"anchor\": \"y\", \"domain\": [0.0, 1.0], \"title\": {\"text\": \"x\"}}, \"yaxis\": {\"anchor\": \"x\", \"domain\": [0.0, 1.0], \"title\": {\"text\": \"y\"}}},\n",
       "                        {\"responsive\": true}\n",
       "                    ).then(function(){\n",
       "                            \n",
       "var gd = document.getElementById('a0e1a386-507e-4c64-b735-5dadc2cc547e');\n",
       "var x = new MutationObserver(function (mutations, observer) {{\n",
       "        var display = window.getComputedStyle(gd).display;\n",
       "        if (!display || display === 'none') {{\n",
       "            console.log([gd, 'removed!']);\n",
       "            Plotly.purge(gd);\n",
       "            observer.disconnect();\n",
       "        }}\n",
       "}});\n",
       "\n",
       "// Listen for the removal of the full notebook cells\n",
       "var notebookContainer = gd.closest('#notebook-container');\n",
       "if (notebookContainer) {{\n",
       "    x.observe(notebookContainer, {childList: true});\n",
       "}}\n",
       "\n",
       "// Listen for the clearing of the current output cell\n",
       "var outputEl = gd.closest('.output');\n",
       "if (outputEl) {{\n",
       "    x.observe(outputEl, {childList: true});\n",
       "}}\n",
       "\n",
       "                        })\n",
       "                };\n",
       "                });\n",
       "            </script>\n",
       "        </div>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import texthero as hero\n",
    "import pandas as pd\n",
    "\n",
    "df = pd.read_csv(\n",
    "    \"https://github.com/jbesomi/texthero/raw/master/dataset/bbcsport.csv\"\n",
    ")\n",
    "df['pca'] = (\n",
    "    df['text']\n",
    "    .pipe(hero.clean)\n",
    "    .pipe(hero.tfidf)\n",
    "    .pipe(hero.pca)\n",
    ")\n",
    "\n",
    "hero.scatterplot(df, col='pca', color='topic', title=\"PCA BBC Sport news\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
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    "name": "ipython",
    "version": 3
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   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.3"
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 },
 "nbformat": 4,
 "nbformat_minor": 2
}
